daanye / lightweightfacedetector

Ultra Light Weight Face Detection with Landmark

LightWeightFaceDetector

update20210510

Upload a new face detection dataset with face box and 5 landmarks. this dataset makes up of big face and helps to improve detection acc with close face.You can add this dataset to widerface.

MobileFaceDet passwords: eu8w

开源一个近场景人脸检测和关键点数据集，数据集包含了27k+张人脸标注，全部为近距离人脸，类似于手机前置拍摄。有助于改善移动端人脸检测和关键点回归精度，将这个数据集和widerface train数据集合并，可以训练了一个大小仅仅120k的人脸检测模型. 数据集标注如下：

Ultra Light Weight Face Detection with Landmark, model size is around 1M+ for Mobile or Edge devices. I samplified RetinaFace structure for fast inference.

I test four light-weight network as backbone including mobilenet v1, v2, v3 and efficientnet-b0.

适用于移动端或者边缘计算的轻量人脸检测和关键点检测模型，模型仅仅1M多。主要基于RetinaFace结构简化，删除了前面几个大特征图上的Head，因此小目标的人脸检测可能会有影响，在一般应用场景下影响不大。

这里速度最快的是mobilenet_v2_0.1,效果如图：

WiderFace Val Performance

Models	Easy	Medium	Hard
mobilenetv1_0.25	0.91718	0.79766	0.3592
mobilenetv2_0.1	0.85330	0.68946	0.2993
mobilenetv3_small	0.93419	0.83259	0.3850
efficientnet-b0	0.93167	0.81466	0.37020

Data

1. Download the WIDERFACE dataset.
2. Here we use the organized dataset we used as in the above directory structure.

Link: from google cloud or baidu cloud Password: ruck

Training

We provide four light weight backbone(mobilenetv1, mobilenetv2, mobilenetv3, efficientnetb0) network to train model.

1.make dir ./weights/ and download imagenet pretrained weights from [link](链接: https://pan.baidu.com/s/1zhyL9ULuIi1KdtXzhSQ4yQ 提取码: urei) and put them in ./weights/

  ./weights/
      mobilenet0.25_Final.pth
      mobilenetV1X0.25_pretrain.tar
      efficientnetb0_face.pth
      mobilenetv3.pth
      mobilenetv2_0.1_face.pth
      ...

1. Before training, you can check network configuration (e.g. batch_size, min_sizes and steps etc..) in data/config.py and train.py.

2. Train the model using WIDER FACE:

CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --network mobilenetv1
CUDA_VISIBLE_DEVICES=0 python train.py --network mobilenetv1

Evaluation

Evaluation widerface val

1. Generate txt file

python test_widerface.py --trained_model weight_file --network mobilenetv1(or mobilenetv2, mobilenetv3, efficientnetb0)

2. Evaluate txt results.

cd ./widerface_evaluate
python setup.py build_ext --inplace
python evaluation.py

Android and IOS

Android is deployed with libtorch：https://github.com/midasklr/facedetection_android.pytorch IOS use ncnn

References

Pytorch_Retinaface